A Stochastic Concept of Capacity, Proceedings of the 16Th International Symposium on Transportation and Traffic Theory, Pp
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Reliability of Freeway Traffic Flow 1 Citation: Brilon, W., Geistefeldt, J., Regler, M.: Reliability of Freeway Traffic Flow: A stochastic Concept of Capacity, Proceedings of the 16th International Symposium on Transportation and Traffic Theory, pp. 125 – 144, College Park, Maryland, July 2005, ISBN 0-08-044680-9 RELIABILITY OF FREEWAY TRAFFIC FLOW: A STOCHASTIC CONCEPT OF CAPACITY Werner Brilon, Justin Geistefeldt, and Matthias Regler, Institute for Transportation and Traffic Engineering, Ruhr-University Bochum, Germany ABSTRACT The paper introduces a new understanding of freeway capacity. Here capacity is understood as the traffic volume below which traffic still flows and above which the flow breaks down into stop-and-go or even standing traffic. It is easy to understand that a capacity in this sense is by no means a constant value. Empirical analysis of traffic flow patterns, counted at 5- minute intervals over several months and at many sites, clearly shows that this type of capacity is Weibull-distributed with a nearly constant shape parameter, which represents the variance. This was identified using the so-called Product Limit Method, which is based on the statistics of lifetime data analysis. It is demonstrated that this method is applicable to all types of freeways. The stochastic methodology allows for a derivation of a theoretical transformation between capacities identified for different interval durations. The technique can also be used to identify effects of different external conditions like speed limits or weather on the capacity of a freeway. The statistical distribution of capacity directly indicates the reliability of the freeway section under investigation. This distribution for one section is then transformed into statistical measures of reliability for larger parts of a network composed of sections of different capacity. Thus, the stochastic concept is also expanded into reliabilities of freeway networks. It is found that a freeway operates at the highest expected efficiency if it is only loaded to 90 % of the conventionally estimated (constant-value) capacity. On the one hand, the paper quotes some real world results from German freeways. On the other hand, the reliability-based analysis leads to a new sophisticated concept for highway traffic engineering. 2 1 INTRODUCTION Conventional measures of effectiveness for freeway facilities usually reflect travel time in the form of e.g. travel velocity or delay. Recently it has been becoming more and more obvious that these parameters are not sufficient for freeway traffic performance assessment since they place great emphasis on smaller differences in travel time, whereas the more significant difference between flowing traffic and congestion is not adequately represented. In addition, traditional quality assessment fails whenever demand exceeds capacity because it will simply attest a failure in this case. However, temporary freeway overloads are quite common. This is why quality assessments for different degrees of freeway congestion are required as well (cf. Shaw, 2003). The capacity of a freeway is traditionally treated as a constant value in traffic engineering guidelines around the world, such as the HCM (2000). Doubts about this nature of capacities as constant values were raised by Ponzlet (1996) who demonstrated that capacities vary according to external conditions like dry or wet road surfaces, daylight or darkness, and prevailing purpose of the freeway (long distance or metropolitan commuter traffic). Moreover, several authors affirmed that even under constant external conditions, different capacities can be observed on freeways in reality (Elefteriadou et al., 1995; Minderhoud et al., 1997; Persaud et al., 1998; Kuehne and Anstett, 1999; Lorenz and Elefteriadou, 2000; Okamura et al., 2000). Most of these authors only observed traffic breakdowns at different flow rates to demonstrate the variability of flows preceding a breakdown. For a more systematic analysis, however, a comprehensive theoretical concept is required. 2 BASIC CONCEPT OF STOCHASTIC CAPACITY Corresponding to the HCM (2000), the capacity of a freeway is defined as the maximum flow rate that can reasonably be expected to traverse a facility under prevailing roadway, traffic, and control conditions. With an identical meaning, the term “capacity”, i.e. the maximum flow rate, could also be defined as the traffic volume below which the performance of the facility is acceptable and above which – in case of greater demand – proper operation fails. The transition between proper operation and non-acceptable flow conditions is called “breakdown”. On a freeway, such a breakdown occurs when the average travel velocity is reduced from an acceptable speed level to a much lower value of congested conditions. These transitions usually involve a rather sudden speed reduction. The suddenness of this breakdown, however, may differ from one country to another depending on the general driving culture. From this definition, it is clear that capacity in this sense is by no means a constant value. A constant value would mean that, given a capacity of e.g. 3,600 veh/h, the traffic should be fluent at a demand of 3,599 veh/h and be congested at a demand of 3,601 veh/h. This clearly indicates that the demand volume that causes breakdown varies in real traffic flow and that Reliability of Freeway Traffic Flow 3 the flow rate of a breakdown depends on the behavior of several drivers combined with the specific local constellation on the freeway. Thus, it is plausible that the breakdown volume should have all properties of a random variable. For using this concept of freeway capacity randomness it is necessary to know more about the capacity distribution function. Its determination is, however, not a trivial task. It is clear that any analytical approach must be supported by a broad empirical investigation. Observations of traffic flow on freeways deliver pairs of values of traffic flow rates and average speeds during predetermined observation intervals (index i). According to the definition of capacity, the observed volume will be below capacity if the average speed exceeds a certain threshold value (e.g. about 70 km/h for German freeway conditions). With an average speed lower than the threshold value, traffic flow is congested. Thus, the flow must have exceeded capacity during the time between two such observations. The capacity itself, however, can not be measured directly. In addition, higher demand volumes are less likely to be observed in the field since there is also a higher probability that, before they occur, the breakdown has already happened during preceding intervals at lower volumes. Both effects make it difficult to estimate the capacity distribution function, which is defined as: Fc (q) = p(c ≤ q) (1) where Fc(q) = capacity distribution function [-] c = capacity [veh/h] q = traffic volume [veh/h] A practicable estimation method was first presented by van Toorenburg (1986) and discussed by Minderhoud et al. (1997). The investigations presented here are based on this idea. However, it seemed advisable to modify some of the basic assumptions of the method. The method proposed by van Toorenburg (1986) is based on an analogy to the statistics of lifetime data analysis. This statistics, in its basic formulation, serves to describe the statistical properties of the duration of human life. Moreover, it is usually applied to analyze the durability of technical components. In this context, the lifetime distribution function is: F(t) = 1−S(t) (2) where F(t) = distribution function of lifetime = p(T ≤ t) T = lifetime S(t) = survival function = p(T > t) 4 Lifetime distributions are often estimated on the basis of experiments of limited duration. Consequently, the lifetimes of several individuals in the sample exceed the duration of the experiment and therefore can not be measured. It is only possible to state that these lifetimes are longer than the duration of the experiment. However, even this information is valuable. These data are called “censored data” (cf. e.g. Lawless, 2003). If a traffic breakdown is regarded as a failure event, the methods for lifetime data analysis can be used to estimate the capacity c, which is the analogon of the lifetime T. The whole analogy between capacity analysis and lifetime data analysis is given in Table 1. Table 1: Analogy between lifetime data analysis and capacity analysis Analysis of Lifetime Data Capacity Analysis Parameter Time t Traffic volume q Failure event Death at time t Breakdown at volume q Lifetime variable Lifetime T Capacity c Lifetime T is longer than the Capacity c is greater than Censoring duration of the experiment traffic demand Survival function S(t) = 1 – F(t) Sc(q) = 1 – Fc(q) Probability density function f(t) fc(q) Probability distribution function F(t) Fc(q) The statistics of lifetime data analysis can be used to estimate distribution functions based on samples that include censored data. A non-parametric method to estimate the survival function is the so-called “Product Limit Method” (PLM) by Kaplan and Meier (1958): n − d Sˆ(t) = j j (3) ∏ n j:t j < t j where Sˆ(t) = estimated survival function [-] nj = number of individuals with a lifetime T ≥ tj [-] dj = number of deaths at time tj [-] Usually, each observed lifetime is used as one tj-value. In this case, dj in Eq. 3 is always equal to 1. Transferred to capacity analysis, Eq. 3 together with Eq. 2 can be written as: k − d F (q) = 1− i i ; i ∈{}B (4) c ∏ k i:qi ≤q i Reliability of Freeway Traffic Flow 5 where Fc(q) = distribution function of capacity c [-] q = traffic volume [veh/h] qi = traffic volume in interval i [veh/h] ki = number of intervals with a traffic volume of q ≥ qi [-] di = number of breakdowns at a volume of qi [-] {B} = set of breakdown intervals (see below) Using this equation, each observed traffic volume q is classified according to B: Traffic is fluent in time interval i, but the observed volume causes a breakdown, i.e.